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Neural network output convert to single matrix

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Camila Gill
Camila Gill 2020-4-21
编辑： Ameer Hamza 2020-4-21
Data file attached.
FIrst, I have two neural networks being tested. I am solving for errors for every iteration. Every iteration produces a unique answer. Once the neural network recalulates for the next iteration, it replaces the prevous solution. I am given only the final solution. I need all iteration solutions in a single column matrix to use for later calculations. How can I combine each answer into a single matrix?
Thought of indexing the results, but I am not sure how to apply it.
Second, how can I determine number of epoch used when trainbr is utilized.
clear
load 'beam_designs_lhs100.mat'; % beam_designs
% Normalize beam models and responses
[beamsin, PS] = mapminmax(beam_designs(:,1:5)');
[beamsout, TS] = mapminmax(beam_designs(:,6:7)');
% 2-layer network
for l1 = 3:5
for l2 = 3:5
% Divide designs into training and test datasets
trainin = beamsin(:,1:600);
trainout = beamsout(:,1:600);
testin = beamsin(:,600+1:end);
testout = beamsout(:,600+1:end);
% Create function fitting neural network
net = fitnet([l1,l2], 'trainlm');
netbr = fitnet([l1,l2], 'trainbr');
net.divideParam.trainRatio = 1;
net.divideParam.valRatio = 0;
net.divideParam.testRatio = 0;
% Train the NN and evaluate its performance
[net, tr] = train(net, trainin, trainout);
[netbr, tr] = train(netbr, trainin, trainout);
outputsD = net(testin(1:5,:));
outputsB = netbr(testin(1:5,:));
perf = perform(net, testout, outputsD); % or use sum of squares
% Computes the sum of squared errors and print results
err_defD = sum((testout(1,:) - outputsD(1,:)).^2);
err_defB = sum((testout(1,:) - outputsB(1,:)).^2);
fprintf('answer %f\n',err_defD) % Dont need, the only way to visualize the solution to each iteration
Anything helps. Thank you.

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采纳的回答

Ameer Hamza
Ameer Hamza 2020-4-21
编辑：Ameer Hamza 2020-4-21
You can do indexing like this
count = 1; % define a counter
% 2-layer network
for l1 = 3:5
for l2 = 3:5
and then change these lines
err_defD(count) = sum((testout(1,:) - outputsD(1,:)).^2);
err_defB(count) = sum((testout(1,:) - outputsB(1,:)).^2);
count = count+1;

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